EnsNet: Ensconce Text in the Wild

A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background. The latter is a novel local-sensitive GAN, which attentively assesses the local consistency of the text erased regions. Both qualitative and quantitative sensitivity experiments on synthetic images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet is essential to achieve a good performance. Moreover, our EnsNet can significantly outperform previous state-of-the-art methods in terms of all metrics. In addition, a qualitative experiment conducted on the SMBNet dataset further demonstrates that the proposed method can also preform well on general object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can preform at 333 fps on an i5-8600 CPU device.Comment: 8 pages, 8 figures, 2 tables, accepted to appear in AAAI 201

Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution

Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education. Most existing works decoupled this problem into several independent sub-tasks of text spotting (text detection and recognition) and information extraction, which completely ignored the high correlation among them during optimization. In this paper, we propose a robust visual information extraction system (VIES) towards real-world scenarios, which is a unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction by taking a single document image as input and outputting the structured information. Specifically, the information extraction branch collects abundant visual and semantic representations from text spotting for multimodal feature fusion and conversely, provides higher-level semantic clues to contribute to the optimization of text spotting. Moreover, regarding the shortage of public benchmarks, we construct a fully-annotated dataset called EPHOIE (https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for both text spotting and visual information extraction. EPHOIE consists of 1,494 images of examination paper head with complex layouts and background, including a total of 15,771 Chinese handwritten or printed text instances. Compared with the state-of-the-art methods, our VIES shows significant superior performance on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used SROIE dataset under the end-to-end scenario.Comment: 8 pages, 5 figures, to be published in AAAI 202

Study on Overburden Fracture and Structural Distribution Evolution Characteristics of Coal Seam Mining in Deep Large Mining Height Working Face

Coal mining has gradually entered the deep mining era, and large-height mining is an important way to mine thick coal seams in the deep. The high coal wall will inevitably make the distribution of the overburden structure in the coal mining face more complicated, and the large buried depth will also cause more intense mine pressure. The study of the distribution and evolution of the overburden structure and stress in the mining site can provide theoretical guidance for safe mining. In this work, a physical similarity modeling test was carried out based on the physical–mechanical parameters of overburden rock and similarity theory, taking the mining of a deep, large-height working face in Pingdingshan Coal Mine as an example. The results show that the deformation and breakage of overburden rock in deep, large-height workings occurring during mining is persistent and not only in a short period of time. The breakage form of overburden can be categorized into two types based on the deformation characteristics: (I) non-separation-induced type, and (II) separation-induced type. Among these, the breakage induced by separation can be divided into two categories: (i) dominated by self-weight stress, and (ii) affected by shear cracks. It also summarizes the form of the overburden structure and the structural morphology of the stope. The overburden structure shows a “combined cantilever beam structure-articulated rock-slab structure-non-articulated rock-slab structure”. Among these, the periodic breakage of the upper cantilever beam evolved articulated and non-articulated rock-slab structure in the lower part, which weakened the supporting effect of the lower gangue and further aggravated the breakage of the upper overburden rock. The shape of the main structure of the stope mainly depends on the fracture line from the advancing coal wall to the upper overburden: from a rectangular shape without collapse to a trapezoidal shape at the initial stage of collapse, to a trapezoidal shape with multiple steps after the main roof collapse

Modeling and Optimization of Impedance Balancing Technique for Common Mode Noise Attenuation in DC-DC Boost Converters

As an effective means of suppressing electromagnetic interference (EMI) noise, the impedance balancing technique has been adopted in the literature. By suppressing the noise source, this technique can theoretically reduce the noise to zero. Nevertheless, its effect is limited in practice and also suffers from noise spikes. Therefore, this paper introduces an accurate frequency modeling method to investigate the attenuation degree of noise source and redesign the impedance selection accordingly in order to improve the noise reduction capability. Based on a conventional boost converter, the common mode (CM) noise model was built by identifying the noise source and propagation paths at first. Then the noise source model was extracted through capturing the switching voltage waveform in time domain and then calculating its Fourier series in frequency domain. After that, the conventional boost converter was modified with the known impedance balancing techniques. This balanced circuit was analyzed with the introduced modeling method, and the equivalent noise source was precisely estimated by combining the noise spectra and impedance information. Furthermore, two optimized schemes with redesigned impedances were proposed to deal with the resonance problem. A hardware circuit was designed and built to experimentally validate the proposed concepts. The experimental results demonstrate the feasibility and effectiveness of the proposed schemes

Investigation of Time-Varying Cable Tension of Bridges Using Time-Frequency Reassignment Techniques Based on Structural Health Monitoring Data

Cables have been increasingly utilized in modern long-span or tied-arch bridges as the main bearing structures. Real-time identification of time-varying cable tension is essential for assessing the service performance of bridges. Vibration-based methods have been an increasing research focus in recent decades. However, a long time interval is needed to estimate structural frequency using vibration-based methods, increasing the calculating time of cable tension. The time-varying cable tension is thus difficult to extract. This study proposes a time-frequency reassignment-based algorithm to reduce the detection time to address this issue. Combined with a time-frequency analysis tool and vibration theory of cables, the algorithm can identify the time-varying frequency and further quickly calculate the time-varying cable tension within 12.8 s. The features of the proposed algorithm are mainly threefold: identifying the time-varying frequencies with high precision; without some prior knowledge of vibration; having no other requirements for sensor modes. Moreover, the experimental validation is conducted using a quasi-static loading in a workshop and a dynamic field test on Sutong Bridge, respectively. The results show that the proposed algorithm can be used to identify time-varying tension and assess the service performance of cables, providing a new path for real-time condition monitoring of bridges in service